Tackling Students' Coding Assignments with LLMs
State-of-the-art large language models (LLMs) have demonstrated an extraordinary ability to write computer code. This ability can be quite beneficial when integrated into an IDE to assist a programmer with basic coding. On the other hand, it may be misused by computer science students for cheating on coding tests or homework assignments. At present, knowledge about the exact capabilities and limitations of state-of-the-art LLMs is still inadequate. Furthermore, their capabilities have been changing quickly with each new release. In this paper, we present a dataset of 559 programming exercises in 10 programming languages collected from a system for evaluating coding assignments at our university. We have experimented with four well-known LLMs (GPT 3.5, GPT 4, Codey, Code Llama) and asked them to solve these assignments. The evaluation results are intriguing and provide insights into the strengths and weaknesses of the models, as well as the dangers and benefits that LLMs pose for computer science education.
Sat 20 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | Session 4: Full Papers + Award & ClosingLLM4Code at Luis de Freitas Branco Chair(s): Prem Devanbu University of California at Davis | ||
16:00 10mTalk | Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers LLM4Code Smitha S Kumar Heriot-Watt University -UAE, Michael Lones Heriot Watt University- UK, Manuel Maarek Heriot-Watt University, Hind Zantout Heriot-Watt University -UAE Pre-print | ||
16:10 10mTalk | Tackling Students' Coding Assignments with LLMs LLM4Code Pre-print | ||
16:20 10mTalk | Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming AssignmentsBest Presentation Award LLM4Code Skyler Grandel Vanderbilt University, Douglas C. Schmidt Vanderbilt University, Kevin Leach Vanderbilt University Pre-print | ||
16:30 10mTalk | An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project LLM4Code Sanka Rasnayaka National University of Singapore, Wang Guanlin National University of Singapore, Ridwan Salihin Shariffdeen National University of Singapore, Ganesh Neelakanta Iyer National University of Singapore Pre-print | ||
16:40 10mTalk | LLMs for Relational Reasoning: How Far are We? LLM4Code Zhiming Li Nanyang Technological University, Singapore, Yushi Cao Nanyang Technological University, Xiufeng Xu Nanyang Technological University, Junzhe Jiang Hong Kong Polytechnic University, Xu Liu North Carolina State University, Yon Shin Teo Continental Automotive Singapore Pte. Ltd., Shang-Wei Lin Nanyang Technological University, Yang Liu Nanyang Technological University Pre-print | ||
16:50 10mTalk | HawkEyes: Spotting and Evading Instruction Disalignments of LLMs LLM4Code Dezhi Ran Peking University, Zihe Song University of Texas at Dallas, Wenhan Zhang Peking University, Wei Yang University of Texas at Dallas, Tao Xie Peking University | ||
17:00 10mTalk | Semantically Aligned Question and Code Generation for Automated Insight GenerationBest Paper Award LLM4Code Ananya Singha Microsoft, Bhavya Chopra Microsoft, Anirudh Khatry Microsoft, Sumit Gulwani Microsoft, Austin Henley University of Tennessee, Vu Le Microsoft, Chris Parnin Microsoft, Mukul Singh Microsoft, Gust Verbruggen Microsoft Pre-print | ||
17:10 20mDay closing | Award & Closing LLM4Code | ||